Ginan YAML Inspector

Use the checkboxes to enable editing and modification of options.

Existing yaml files and their configuration can be loaded by importing them below.

inputs: ⯆ # This section of the yaml file specifies the lists of files to be used for general metadata inputs, and inputs of external product data.
inputs_root: # Root path to be added to all other input files (unless they are absolute)
include_yamls: # List of yaml files to include before this one
gnss_observations: ⯆ # Signal observation data from gnss receivers to be used as measurements
gnss_observations_root: # Root path to be added to all other gnss data inputs (unless they are absolute)
rnx_inputs:
rtcm_inputs:
custom_inputs:
ubx_inputs:
satellite_data: ⯆
rtcm_inputs: ⯆ # This section specifies how State State Representation (SSR) corrections are applied after they are downloaded from an NTRIP caster.
rtcm_inputs_root: # Root path to be added to all other rtcm inputs (unless they are absolute)
rtcm_inputs: # List of rtcm inputs to use for corrections
ssr_antenna_offset: # Ephemeris type that is provided in the listed SSR stream, i.e. satellite antenna-phase-centre (APC) or centre-of-mass (COM). This information is listed in the NTRIP Caster's sourcetable {unspecified, apc, com}
qzl6_inputs: # List of qzss L6 inputs to use for corrections
code_bias_validity_time: # Valid time period of SSR code biases
global_vtec_valid_time: # Valid time period of global VTEC maps
local_stec_valid_time: # Valid time period of local STEC corrections
local_trop_valid_time: # Valid time period of local Troposphere corrections
one_freq_phase_bias: # Used stream have one SSR phase bias per frequency
phase_bias_validity_time: # Valid time period of SSR phase biases
validity_interval_factor:
sisnet_inputs: ⯆ # Configuration for SiSNet stream input. SiSNet broadcast SBAS messages
sisnet_inputs: # List of sisnet inputs to use for corrections
sisnet_inputs_root: # Root path to be added to all other sisnet inputs (unless they are absolute)
sbas_carrier_frequency: # Carrier frequency of SBAS channel
sbas_prn: # PRN for SBAS satelite
satellite_data_root: # Root path to be added to all other satellite data files (unless they are absolute)
bsx_files: # List of biassinex files to use
clk_files: # List of clock files to use
dcb_files: # List of dcb files to use
nav_files: # List of ephemeris files to use
obx_files: # List of orbex files to use
sp3_files: # List of sp3 files to use
com_files: # List of com files to use - retroreflector offsets from centre-of-mass for spherical sats
crd_files: # List of crd files to use - SLR observation data
sid_files: # List of sat ID files to use - from https://cddis.nasa.gov/sp3c_satlist.html/
pseudo_observations: ⯆ # Use data from pre-processed data products as observations. Useful for combining and comparing datasets
pseudo_observations_root: # Root path to be added to all other pseudo obs data files (unless they are absolute)
filter_files: # List of inputs to use for custom pseudoobservations
snx_inputs:
eci_pseudoobs: # Pseudo observations are provided in eci frame rather than standard ECEF SP3 files
sp3_inputs:
tides: ⯆ # Files specifying tidal loading and potential inputs
atl_blq_col_order: # Column order for amplitude and phase components in ATL BLQ files [m2, s2, n2, k2, s1, k1, o1, p1, q1, mf, mm, ssa]
atl_blq_row_order: # Row order for amplitude and phase components in ATL BLQ files [east, west, north, south, up, down]
atmos_ocean_dealiasing_files: # List of tide files to use
atmos_tide_loading_blq_files: # List of atl blq files to use
atmos_tide_potential_files: # List of tide files to use
ocean_pole_tide_loading_files: # List of opole files to use
ocean_pole_tide_potential_files: # List of tide files to use
ocean_tide_loading_blq_files: # List of otl blq files to use
ocean_tide_potential_files: # List of tide files to use
otl_blq_col_order: # Column order for amplitude and phase components in OTL BLQ files [m2, s2, n2, k2, s1, k1, o1, p1, q1, mf, mm, ssa]
otl_blq_row_order: # Row order for amplitude and phase components in OTL BLQ files [east, west, north, south, up, down]
troposphere: ⯆ # Files specifying tropospheric model inputs
gpt2grid_files: # List of gpt2 grid files to use
orography_files: # List of orography files to use
vmf_files: # List of vmf files to use
ionosphere: ⯆ # Files specifying ionospheric model inputs
atm_reg_definitions: # List of files to define regions for compact SSR
ion_files: # List of IONEX files for VTEC input
atx_files: # List of atx files to use
erp_files: # List of erp files to use
cmc_files: # List of cmc files to use
egm_files: # List of egm files to use
hfeop_files: # List of hfeop files to use
igrf_files: # List of igrf files to use
planetary_ephemeris_files: # List of jpl files to use
snx_files: # List of snx files to use
space_weather_files: # List of space weather files to use
allow_missing_inputs: # Allow adding inpuut files which do not (yet) exist
outputs: ⯆ # Specifies options to enable outputs and specify file locations. Each section typically contains an option to `output` the filetype, and a `directory` to place the files named `filename`, along with any ancillary options.
outputs_root: # Directory that outputs will be placed in
colourise_terminal: # Use ascii command codes to highlight warnings and errors
warn_once: # Print warnings once only
metadata: ⯆ # Options for setting metadata for inputs and outputs
config_description: # ID for this config, used to replace tags in other options
pass: # Password for connecting to NTRIP casters
user: # Username for connecting to NTRIP casters
ac_contact: # Contact person for output files headers
analysis_agency: # Agency for output files headers
analysis_centre: # Analysis centre for output files headers
analysis_software: # Program for output files headers
analysis_software_version: # Version for output files headers
atmospheric_tide_loading_model: # Atmospheric tide loading model applied
config_details: # Comments and details specific to the config
geoid_model: # Geoid model name for undulation values
gradient_mapping_function: # Name of mapping function used for mapping horizontal troposphere gradients
ocean_tide_loading_model: # Ocean tide loading model applied
reference_system: # Terrestrial Reference System Code
rinex_comment: # Comment for output files headers
time_system: # Time system - e.g. "G", "UTC"
trace: ⯆ # Trace files are used to document processing
directory: # Directory to output trace files to
level: # Threshold level for printing messages (0-6). Increasing this increases the amount of data stored in all trace files
output_config: # Output configuration files to top of trace files
output_initialised_states: # Output states after state transition 2
output_predicted_states: # Output states after state transition 1
output_residual_chain: # Output component-wise details for measurement residuals
output_residuals: # Output measurements and residuals
output_statistics: # Output statistics accumulated each epoch
output_summaries: # Output summaries accumulated each epoch
output_network: # Output trace files for complete network of receivers, inclucing kalman filter results and statistics
output_receivers: # Output trace files for individual receivers processing
output_ionosphere: # Output trace files for ionosphere processing, inclucing kalman filter results and statistics
output_satellites: # Output trace files for individual satellites processing
json_filename: # Template filename for receiver json files
network_filename: # Template filename for network trace files
receiver_filename: # Template filename for receiver trace files
ionosphere_filename: # Template filename for ionosphere trace files
satellite_filename: # Template filename for satellite trace files
output_json: # Output json formatted trace files
output_rotation: ⯆ # Trace files can be rotated periodically by epoch interval. These options specify the period that applies to the template variables in filenames
period: # Period that times will be rounded by to generate template variables in filenames
period_units: # {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
ssr_outputs: ⯆ # This section specifies how State State Representation (SSR) corrections are calculated before being published to an NTRIP caster.
code_bias_sources: # Sources for SSR code biases [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
phase_bias_sources: # Sources for SSR phase biases [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
atmospheric: ⯆
cmpssr_stec_format: # Format of STEC gridded corrections: 0:4bit(LSB=0.04) , 1:4bit(LSB=0.12), 2:5bit, 3:7bit, 4:16bit
cmpssr_trop_format: # Format of Trop. ZWD corrections: 0:8bit, 1:6bit
grid_type: # Grid type for gridded atmospheric corrections
lat_int:
lat_max:
lat_min:
lon_int:
lon_max:
lon_min:
npoly_iono:
npoly_trop:
region_id: # Region ID for atmospheric corrections
region_iod: # Region IOD for atmospheric corrections (default: -1 for undefined)
sources: # Sources for SSR ionosphere [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
use_grid_iono: # Grid type for gridded atmospheric corrections
use_grid_trop: # Grid type for gridded atmospheric corrections
clock_sources: # Sources for SSR clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
cmpssr_cell_mask:
ephemeris_sources: # Sources for SSR ephemeris [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
extrapolate_corrections:
max_stec_sigma:
prediction_duration:
prediction_interval:
streams: ⯆
root_url: # Root url to be prepended to all other streams specified in this section. If the streams used have individually specified root urls, usernames, or passwords, this should not be used.
labels: # List of output stream is with further information to be found in its own section, as per XMPL below
xmpl: ⯆
messages: ⯆
rtcm_0: ⯆ # Message type to output
udi: # Update interval
rtcm_1019: ⯆ # Message type to output
udi: # Update interval
rtcm_1020: ⯆ # Message type to output
udi: # Update interval
rtcm_1042: ⯆ # Message type to output
udi: # Update interval
rtcm_1044: ⯆ # Message type to output
udi: # Update interval
rtcm_1045: ⯆ # Message type to output
udi: # Update interval
rtcm_1046: ⯆ # Message type to output
udi: # Update interval
rtcm_1057: ⯆ # Message type to output
udi: # Update interval
rtcm_1058: ⯆ # Message type to output
udi: # Update interval
rtcm_1059: ⯆ # Message type to output
udi: # Update interval
rtcm_1060: ⯆ # Message type to output
udi: # Update interval
rtcm_1061: ⯆ # Message type to output
udi: # Update interval
rtcm_1062: ⯆ # Message type to output
udi: # Update interval
rtcm_1063: ⯆ # Message type to output
udi: # Update interval
rtcm_1064: ⯆ # Message type to output
udi: # Update interval
rtcm_1065: ⯆ # Message type to output
udi: # Update interval
rtcm_1066: ⯆ # Message type to output
udi: # Update interval
rtcm_1067: ⯆ # Message type to output
udi: # Update interval
rtcm_1068: ⯆ # Message type to output
udi: # Update interval
rtcm_1074: ⯆ # Message type to output
udi: # Update interval
rtcm_1075: ⯆ # Message type to output
udi: # Update interval
rtcm_1076: ⯆ # Message type to output
udi: # Update interval
rtcm_1077: ⯆ # Message type to output
udi: # Update interval
rtcm_1084: ⯆ # Message type to output
udi: # Update interval
rtcm_1085: ⯆ # Message type to output
udi: # Update interval
rtcm_1086: ⯆ # Message type to output
udi: # Update interval
rtcm_1087: ⯆ # Message type to output
udi: # Update interval
rtcm_1094: ⯆ # Message type to output
udi: # Update interval
rtcm_1095: ⯆ # Message type to output
udi: # Update interval
rtcm_1096: ⯆ # Message type to output
udi: # Update interval
rtcm_1097: ⯆ # Message type to output
udi: # Update interval
rtcm_1114: ⯆ # Message type to output
udi: # Update interval
rtcm_1115: ⯆ # Message type to output
udi: # Update interval
rtcm_1116: ⯆ # Message type to output
udi: # Update interval
rtcm_1117: ⯆ # Message type to output
udi: # Update interval
rtcm_1124: ⯆ # Message type to output
udi: # Update interval
rtcm_1125: ⯆ # Message type to output
udi: # Update interval
rtcm_1126: ⯆ # Message type to output
udi: # Update interval
rtcm_1127: ⯆ # Message type to output
udi: # Update interval
rtcm_1240: ⯆ # Message type to output
udi: # Update interval
rtcm_1241: ⯆ # Message type to output
udi: # Update interval
rtcm_1242: ⯆ # Message type to output
udi: # Update interval
rtcm_1243: ⯆ # Message type to output
udi: # Update interval
rtcm_1244: ⯆ # Message type to output
udi: # Update interval
rtcm_1245: ⯆ # Message type to output
udi: # Update interval
rtcm_1246: ⯆ # Message type to output
udi: # Update interval
rtcm_1247: ⯆ # Message type to output
udi: # Update interval
rtcm_1248: ⯆ # Message type to output
udi: # Update interval
rtcm_1249: ⯆ # Message type to output
udi: # Update interval
rtcm_1250: ⯆ # Message type to output
udi: # Update interval
rtcm_1251: ⯆ # Message type to output
udi: # Update interval
rtcm_1252: ⯆ # Message type to output
udi: # Update interval
rtcm_1253: ⯆ # Message type to output
udi: # Update interval
rtcm_1254: ⯆ # Message type to output
udi: # Update interval
rtcm_1255: ⯆ # Message type to output
udi: # Update interval
rtcm_1256: ⯆ # Message type to output
udi: # Update interval
rtcm_1257: ⯆ # Message type to output
udi: # Update interval
rtcm_1258: ⯆ # Message type to output
udi: # Update interval
rtcm_1259: ⯆ # Message type to output
udi: # Update interval
rtcm_1260: ⯆ # Message type to output
udi: # Update interval
rtcm_1261: ⯆ # Message type to output
udi: # Update interval
rtcm_1262: ⯆ # Message type to output
udi: # Update interval
rtcm_1263: ⯆ # Message type to output
udi: # Update interval
rtcm_1265: ⯆ # Message type to output
udi: # Update interval
rtcm_1266: ⯆ # Message type to output
udi: # Update interval
rtcm_1267: ⯆ # Message type to output
udi: # Update interval
rtcm_1268: ⯆ # Message type to output
udi: # Update interval
rtcm_1269: ⯆ # Message type to output
udi: # Update interval
rtcm_1270: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_00: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_01: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_02: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_03: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_04: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_05: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_06: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_07: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_08: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_09: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_10: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_11: ⯆ # Message type to output
udi: # Update interval
rtcm_4073_12: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_000: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_001: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_002: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_003: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_004: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_005: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_006: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_007: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_008: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_020: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_021: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_022: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_023: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_024: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_025: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_026: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_027: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_040: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_041: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_042: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_043: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_044: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_045: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_046: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_047: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_060: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_061: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_062: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_063: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_064: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_065: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_066: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_067: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_080: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_081: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_082: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_083: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_084: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_085: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_086: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_087: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_100: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_101: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_102: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_103: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_104: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_105: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_106: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_107: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_120: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_121: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_122: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_123: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_124: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_125: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_126: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_127: ⯆ # Message type to output
udi: # Update interval
rtcm_4076_201: ⯆ # Message type to output
udi: # Update interval
rtcm_4082: ⯆ # Message type to output
udi: # Update interval
url: # Url of caster to send messages to
itrf_datum:
provider_id:
solution_id:
clocks: ⯆ # Rinex formatted clock files
output: # Output clock files
directory: # Directory to output clock files to
filename: # Template filename for clock files
output_interval: # Update interval for clock records
receiver_sources: # [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
satellite_sources: # [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
gpx: ⯆ # GPX files contain point data that may be easily viewed in GIS mapping software
output:
directory:
filename:
log: ⯆ # Log files store console output in files
json: # Log with json metadata
output: # Enable console output logging
directory: # Log output directory
filename: # Log output filename
pos: ⯆ # POS files contain point data that may be easily viewed in GIS mapping software
output:
directory:
filename:
bias_sinex: ⯆ # Rinex formatted bias sinex files
output: # Output bias sinex files
bias_time_system: # Time system for bias SINEX "G", "C", "R", "UTC", "TAI"
code_output_interval: # Update interval for code biases
directory: # Directory to output bias sinex files to
filename: # Template filename for bias sinex files
output_rec_bias: # output receiver biases
phase_output_interval: # Update interval for phase biases
cost: ⯆ # COST format files are used to export troposhere products, such as ZTD and delay gradients.
output: # Enable data exporting to troposphere COST file
cost_centre: # Processing centre
cost_format: # Format name & version number
cost_met_sources: # Source of met. data
cost_method: # Processing method
cost_orbit_type: # Orbit type
cost_project: # Project name
cost_status: # File status
directory: # Directory to export troposphere COST file
filename: # Troposphere COST filename
sources: # Source for troposphere delay data - KALMAN, etc. [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
time_interval: # Time interval between entries in troposphere COST file (sec)
erp: ⯆ # Earth rotation parameters can be output to file
output: # Enable exporting of erp data
directory: # Directory to export erp data files
filename: # ERP data output filename
orbex: ⯆
output: # Output orbex file
attitude_sources: # Sources for orbex attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock_sources: # Sources for orbex clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
directory: # Output orbex directory
filename: # Output orbex filename
orbit_sources: # Sources for orbex orbits [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
output_interval: # Update interval for orbex records (irregular epoch interval is currently NOT supported)
record_types: # List of record types to output to orbex file [pcs, vcs, cpc, cvc, pos, vel, clk, crt, att]
sinex: ⯆
output:
directory:
filename:
sp3: ⯆ # SP3 files contain orbital and clock data of satellites and receivers
output: # Enable SP3 file outputs
clock_sources: # List of sources for clock data for SP3 outputs [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
directory: # Directory to store SP3 outputs
filename: # SP3 output filename
orbit_sources: # List of sources for orbit data for SP3 outputs [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
output_inertial: # Output the entries using inertial positions and velocities
output_interval: # Update interval for SP3 records
output_velocities: # Output velocity data to SP3 file
predicted_filename: # Filename for predicted SP3 outputs
trop_sinex: ⯆ # Troposphere SINEX files are used to export troposhere products, such as ZTD and delay gradients.
output: # Enable data exporting to troposphere SINEX file
const_code: # Troposphere SINEX const code
directory: # Directory to export troposphere SINEX file
filename: # Troposphere SINEX filename
obs_code: # Troposphere SINEX observation code
sol_type: # Troposphere SINEX solution type
sources: # Source for troposphere delay data - KALMAN, etc. [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
version: # Troposphere SINEX version
ionex: ⯆ # IONEX formatted ionospheric mapping and modelling outputs
output: # Enable exporting ionospheric model data
directory: # Directory to export ionex data
filename: # Ionex data filename
grid: ⯆
lat_centre: # Center lattitude for models
lat_resolution: # Interval between lattitude outputs
lat_width: # Total lattitudinal width of model
lon_centre: # Center longitude for models
lon_resolution: # Interval between longitude outputs
lon_width: # Total longitudinal width of model
time_resolution: # Interval between output epochs
ionstec: ⯆
output:
directory:
filename:
orbit_ics: ⯆ # Orbital parameters can be output in a yaml that Ginan can later use as an initial condition for futher processing.
directory: # Output orbital initial condition directory
filename: # Output orbital initial condition filename
output: # Output orbital initial condition file
sbas_ems: ⯆
output:
directory:
filename:
network_statistics: ⯆
output: # Enable exporting network statistics data to file
directory: # Directory to export network statistics data
filename: # Network statistics data filename
ntrip_log: ⯆
output:
directory:
filename:
rinex_nav: ⯆
output:
directory:
filename:
version:
rinex_obs: ⯆
output:
directory:
filename:
output_doppler:
output_phase_range:
output_pseudorange:
output_signal_to_noise:
version:
rtcm_nav: ⯆
output:
directory:
filename:
rtcm_obs: ⯆
output:
directory:
filename:
decoded_rtcm: ⯆ # RTCM messages that are received may be recorded to human-readable json files
output: # Enable exporting decoded RTCM data to file
directory: # Directory to export decoded RTCM data
filename: # Decoded RTCM data filename
encoded_rtcm: ⯆ # RTCM messages that are encoded and transmitted may be recorded to human-readable json files
output: # Enable exporting encoded RTCM data to file
directory: # Directory to export encoded RTCM data
filename: # Encoded RTCM data filename
raw_custom: ⯆
output:
directory:
filename:
raw_ubx: ⯆
output:
directory:
filename:
slr_obs: ⯆ # SLR_OBS files are used as temporary files to arrange SLR observations by time. SLR observations are taken from CRD files, which are not strictly in time-order).
output: # Enable data exporting to tabular SLR obs file
directory: # Directory to export tabular SLR obs file
filename: # Tabular SLR obs filename
processing_options: ⯆ # Various sections and parameters to specify how the observations are processed
epoch_control: ⯆ # Specifies the rate and duration of data processing
end_epoch: # (YYYY-MM-DD hh:mm:ss) The time of the last epoch to process (all observations after this will be skipped)
epoch_interval: # Desired time step between each processing epoch
max_epochs: # Maximum number of epochs to process
start_epoch: # (YYYY-MM-DD hh:mm:ss) The time of the first epoch to process (all observations before this will be skipped)
sleep_milliseconds: # Time to sleep before checking for new data - lower numbers are associated with high idle cpu usage
assign_closest_epoch: # Assign observations to the closest epoch - don't skip observations that fall between epochs
epoch_tolerance: # Tolerance of times to add to an epoch (usually half of the original data's sample rate)
max_rec_latency: # Time to wait from the reception of the first data of an epoch before skipping receivers with data still unreceived
require_obs: # Exit the program if no observation sources are available
simulate_real_time: # For RTCM playback - delay processing to match original data rate
wait_next_epoch: # Time to wait for next epochs data before skipping the epoch (will default to epoch_interval as an appropriate minimum value for realtime)
gnss_general: ⯆ # Options to specify the processing of gnss observations
sys_options: ⯆
bds: ⯆ # Options for the BDS constellation
process: # Process this constellation
code_priorities: # List of observation codes to use in processing [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
network_amb_pivot: # Constrain: set of ambiguities, to eliminate network rank deficiencies
receiver_amb_pivot: # Constrain: set of ambiguities, to eliminate receiver rank deficiencies
reject_eclipse: # Exclude satellites that are in eclipsing region
use_for_iono_model: # Use this constellation as part of Ionospheric model
use_iono_corrections: # Use external ionosphere delay estimation for this constellation
used_nav_type: # {none, lnav, fdma, fnav, inav, ifnv, d1, d2, d1d2, sbas, cnav, cnv1, cnv2, cnv3, cnvx}
ambiguity_resolution: # Solve carrier phase ambiguities for this constellation
constrain_best_ambiguity_integer: # Constrain the best ambiguity of a sys/code pair to an integer once
constrain_clock: # ID of a sat/rec for constraining its clock
constrain_phase_bias: # ID of a sat/rec for constraining its phase bias
gal: ⯆ # Options for the GAL constellation
process: # Process this constellation
code_priorities: # List of observation codes to use in processing [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
network_amb_pivot: # Constrain: set of ambiguities, to eliminate network rank deficiencies
receiver_amb_pivot: # Constrain: set of ambiguities, to eliminate receiver rank deficiencies
reject_eclipse: # Exclude satellites that are in eclipsing region
use_for_iono_model: # Use this constellation as part of Ionospheric model
use_iono_corrections: # Use external ionosphere delay estimation for this constellation
used_nav_type: # {none, lnav, fdma, fnav, inav, ifnv, d1, d2, d1d2, sbas, cnav, cnv1, cnv2, cnv3, cnvx}
ambiguity_resolution: # Solve carrier phase ambiguities for this constellation
constrain_best_ambiguity_integer: # Constrain the best ambiguity of a sys/code pair to an integer once
constrain_clock: # ID of a sat/rec for constraining its clock
constrain_phase_bias: # ID of a sat/rec for constraining its phase bias
glo: ⯆ # Options for the GLO constellation
process: # Process this constellation
code_priorities: # List of observation codes to use in processing [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
network_amb_pivot: # Constrain: set of ambiguities, to eliminate network rank deficiencies
receiver_amb_pivot: # Constrain: set of ambiguities, to eliminate receiver rank deficiencies
reject_eclipse: # Exclude satellites that are in eclipsing region
use_for_iono_model: # Use this constellation as part of Ionospheric model
use_iono_corrections: # Use external ionosphere delay estimation for this constellation
used_nav_type: # {none, lnav, fdma, fnav, inav, ifnv, d1, d2, d1d2, sbas, cnav, cnv1, cnv2, cnv3, cnvx}
ambiguity_resolution: # Solve carrier phase ambiguities for this constellation
constrain_best_ambiguity_integer: # Constrain the best ambiguity of a sys/code pair to an integer once
constrain_clock: # ID of a sat/rec for constraining its clock
constrain_phase_bias: # ID of a sat/rec for constraining its phase bias
gps: ⯆ # Options for the GPS constellation
process: # Process this constellation
code_priorities: # List of observation codes to use in processing [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
network_amb_pivot: # Constrain: set of ambiguities, to eliminate network rank deficiencies
receiver_amb_pivot: # Constrain: set of ambiguities, to eliminate receiver rank deficiencies
reject_eclipse: # Exclude satellites that are in eclipsing region
use_for_iono_model: # Use this constellation as part of Ionospheric model
use_iono_corrections: # Use external ionosphere delay estimation for this constellation
used_nav_type: # {none, lnav, fdma, fnav, inav, ifnv, d1, d2, d1d2, sbas, cnav, cnv1, cnv2, cnv3, cnvx}
ambiguity_resolution: # Solve carrier phase ambiguities for this constellation
constrain_best_ambiguity_integer: # Constrain the best ambiguity of a sys/code pair to an integer once
constrain_clock: # ID of a sat/rec for constraining its clock
constrain_phase_bias: # ID of a sat/rec for constraining its phase bias
leo: ⯆ # Options for the LEO constellation
process: # Process this constellation
code_priorities: # List of observation codes to use in processing [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
network_amb_pivot: # Constrain: set of ambiguities, to eliminate network rank deficiencies
receiver_amb_pivot: # Constrain: set of ambiguities, to eliminate receiver rank deficiencies
reject_eclipse: # Exclude satellites that are in eclipsing region
use_for_iono_model: # Use this constellation as part of Ionospheric model
use_iono_corrections: # Use external ionosphere delay estimation for this constellation
used_nav_type: # {none, lnav, fdma, fnav, inav, ifnv, d1, d2, d1d2, sbas, cnav, cnv1, cnv2, cnv3, cnvx}
ambiguity_resolution: # Solve carrier phase ambiguities for this constellation
constrain_best_ambiguity_integer: # Constrain the best ambiguity of a sys/code pair to an integer once
constrain_clock: # ID of a sat/rec for constraining its clock
constrain_phase_bias: # ID of a sat/rec for constraining its phase bias
qzs: ⯆ # Options for the QZS constellation
process: # Process this constellation
code_priorities: # List of observation codes to use in processing [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
network_amb_pivot: # Constrain: set of ambiguities, to eliminate network rank deficiencies
receiver_amb_pivot: # Constrain: set of ambiguities, to eliminate receiver rank deficiencies
reject_eclipse: # Exclude satellites that are in eclipsing region
use_for_iono_model: # Use this constellation as part of Ionospheric model
use_iono_corrections: # Use external ionosphere delay estimation for this constellation
used_nav_type: # {none, lnav, fdma, fnav, inav, ifnv, d1, d2, d1d2, sbas, cnav, cnv1, cnv2, cnv3, cnvx}
ambiguity_resolution: # Solve carrier phase ambiguities for this constellation
constrain_best_ambiguity_integer: # Constrain the best ambiguity of a sys/code pair to an integer once
constrain_clock: # ID of a sat/rec for constraining its clock
constrain_phase_bias: # ID of a sat/rec for constraining its phase bias
sbs: ⯆ # Options for the SBS constellation
process: # Process this constellation
code_priorities: # List of observation codes to use in processing [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
network_amb_pivot: # Constrain: set of ambiguities, to eliminate network rank deficiencies
receiver_amb_pivot: # Constrain: set of ambiguities, to eliminate receiver rank deficiencies
reject_eclipse: # Exclude satellites that are in eclipsing region
use_for_iono_model: # Use this constellation as part of Ionospheric model
use_iono_corrections: # Use external ionosphere delay estimation for this constellation
used_nav_type: # {none, lnav, fdma, fnav, inav, ifnv, d1, d2, d1d2, sbas, cnav, cnv1, cnv2, cnv3, cnvx}
ambiguity_resolution: # Solve carrier phase ambiguities for this constellation
constrain_best_ambiguity_integer: # Constrain the best ambiguity of a sys/code pair to an integer once
constrain_clock: # ID of a sat/rec for constraining its clock
constrain_phase_bias: # ID of a sat/rec for constraining its phase bias
code_measurements: ⯆
process: # Process code measurements
phase_measurements: ⯆
process: # Process phase measurements
add_eop_component: # Add eop adjustments as a component in residual chain (for adjusting frames to match ecef ephemeris)
adjust_clocks_for_jumps_only: # Round clock adjustments from SPP to half milliseconds
adjust_rec_clocks_by_spp: # Adjust receiver clocks by spp values to minimise prefit residuals
auto_fill_pco: # Use similar PCOs when requested values are not found
common_rec_pco: # Use L1 receiver PCO values for all signals
common_sat_pco: # Use L1 satellite PCO values for all signals
delete_old_ephemerides: # Remove old ephemerides that have accumulated over time from before far before the currently processing epoch
equate_ionospheres: # Use same STEC values for different receivers, useful for simulated rtk mode
equate_tropospheres: # Use same troposphere values for different receivers, useful for simulated rtk mode
fixed_phase_bias_var: # Variance of phase bias to be considered fixed/binded
gpst_utc_leap_seconds: # Difference between gps time and utc in leap seconds
interpolate_rec_pco: # Interpolate other known pco values to find pco for unknown frequencies
merge_correlated_states: # Combine correlated states to eliminate unestimable states
minimise_ionosphere_offsets: # Apply gauss-markov mu values to stec values to minimise offsets with respect to klobuchar values
minimise_sat_clock_offsets: # Apply gauss-markov mu values to satellite clocks to minimise offsets with respect to broadcast values
minimise_sat_orbit_offsets: # Apply gauss-markov mu values to satellite orbits to minimise offsets with respect to broadcast values
pivot_receiver: # Largely deprecated option for iono
reference_bias: # ID of sat/rec to use for reference bias in pivot calculations
reference_clock: # ID of sat/rec to use for reference clock in pivot calculations
require_antenna_details: # Restrict processing to receivers that have antenna details
require_apriori_positions: # Restrict processing to receivers that have apriori positions available
require_reflector_com: # Restrict processing to SLR observations that have center of mass to laser retroreflector array offsets
require_sinex_data: # Restrict processing to receivers that have sinex data available
require_site_eccentricity: # Restrict processing to receivers that have site eccentricity information
use_primary_signals: # Limit processing to first signal of a frequency when multiple are available
use_rtk_combo: # Combine applicable observations to simulate an rtk solution
use_tgd_bias: # Use TGD/BGD bias from ephemeris, DO NOT turn on unless using Klobuchar/NeQuick Ionospheres
process_modes: ⯆ # Aspects of the processing flow may be enabled and disabled according to desired type of solutions
ppp: # Perform PPP network or end user mode
preprocessor: # Preprocessing and quality checks
spp: # Perform SPP on receiver data
ionosphere: # Compute Ionosphere models based on GNSS measurements
slr: # Process SLR observations
spp: ⯆ # Configurations for the kalman filter and its sub processes
max_lsq_iterations: # Maximum number of iterations of least squares allowed for convergence
outlier_screening: ⯆ # Statistical checks allow for detection of outliers that exceed their confidence intervals.
chi_square: ⯆
enable: # Enable Chi-square test
mode: # Chi-square test mode {innovation, measurement, state}
sigma_threshold: # Chi-square test threshold in terms of 'times of sigma'
postfit: ⯆
max_iterations: # Maximum number of measurements to exclude using postfit checks while iterating filter
meas_sigma_threshold: # Sigma threshold for measurements
sigma_check: # Enable sigma check
sigma_threshold: # Sigma threshold
state_sigma_threshold: # Sigma threshold for states
prefit: ⯆
max_iterations: # Maximum number of measurements to exclude using prefit checks before attempting to filter
meas_sigma_threshold: # Sigma threshold for measurements
omega_test: # Enable omega-test
sigma_check: # Enable sigma check
sigma_threshold: # Sigma threshold
state_sigma_threshold: # Sigma threshold for states
max_gdop: # Maximum dilution of precision before error is flagged
raim: # Enable Receiver Autonomous Integrity Monitoring. When SPP fails further SPP solutions are calculated with subsets of observations with the aim of eliminating a problem satellite
sigma_scaling: # Scale applied to measurement noise for spp
always_reinitialise: # Reset SPP state to zero to avoid potential for lock-in of bad states
iono_mode: # {off, broadcast, sbas, iono_free_linear_combo, estimate, total_electron_content, qzs, lex, stec}
preprocessor: ⯆ # Configurations for the kalman filter and its sub processes
cycle_slips: ⯆ # Cycle slips may be detected by the preprocessor and measurements rejected or ambiguities reinitialised
mw_process_noise: # Process noise applied to filtered Melbourne-Wubenna measurements to detect cycle slips
slip_threshold: # Value used to determine when a slip has occurred
preprocess_all_data:
ppp_filter: ⯆ # Configurations for the kalman filter and its sub processes
ionospheric_components: ⯆ # Slant ionospheric components
common_ionosphere: # Use the same ionosphere state for code and phase observations
use_gf_combo: # Combine 'uncombined' measurements to simulate a geometry-free solution
use_if_combo: # Combine 'uncombined' measurements to simulate an ionosphere-free solution
corr_mode: # {off, broadcast, sbas, iono_free_linear_combo, estimate, total_electron_content, qzs, lex, stec}
outlier_screening: ⯆ # Statistical checks allow for detection of outliers that exceed their confidence intervals.
chi_square: ⯆
enable: # Enable Chi-square test
mode: # Chi-square test mode {innovation, measurement, state}
sigma_threshold: # Chi-square test threshold in terms of 'times of sigma'
postfit: ⯆
max_iterations: # Maximum number of measurements to exclude using postfit checks while iterating filter
meas_sigma_threshold: # Sigma threshold for measurements
sigma_check: # Enable sigma check
sigma_threshold: # Sigma threshold
state_sigma_threshold: # Sigma threshold for states
prefit: ⯆
max_iterations: # Maximum number of measurements to exclude using prefit checks before attempting to filter
meas_sigma_threshold: # Sigma threshold for measurements
omega_test: # Enable omega-test
sigma_check: # Enable sigma check
sigma_threshold: # Sigma threshold
state_sigma_threshold: # Sigma threshold for states
advanced_postfits: # Use alternate calculation method to determine postfit residuals
assume_linearity: # Residuals will be adjusted during measurement combination rather than performing 2 seperate state transitions
chunking: ⯆
by_receiver: # Split large filter and measurement matrices blockwise by receiver ID to improve processing speed
by_satellite: # Split large filter and measurement matrices blockwise by satellite ID to improve processing speed
size:
inverter: # Inverter to be used within the Kalman filter update stage, which may provide different performance outcomes in terms of processing time and accuracy and stability. {none, inv, llt, ldlt, colpivhqr, bdcsvd, jacobisvd, fullpivlu, first_unsupported, fullpivhqr}
joseph_stabilisation:
periodic_reset: ⯆
enable: # Enable periodic reset of filter states
interval: # Interval between reset of filter states
states: # States to remove for periodic reset [none, one, all, rec_pos, rec_vel, rec_pos_rate, rec_acc, strain_rate, pos, vel, acc, heading, orientation, ref_sys_bias, begin_clock_states, rec_clock, rec_sys_bias, rec_clock_rate, rec_sys_bias_rate, rec_clock_rate_gm, rec_sys_bias_rate_gm, sat_clock, sat_clock_rate, sat_clock_rate_gm, end_clock_states, trop, trop_grad, trop_model, ionospheric, iono_stec, rec_pco_x, rec_pco_y, rec_pco_z, sat_pco_x, sat_pco_y, sat_pco_z, rec_pcv, ant_delta, eop, eop_rate, calc, slr_rec_range_bias, slr_rec_time_bias, xform_xlate, xform_rtate, xform_scale, xform_delay, ambiguity, code_bias, phase_bias, z_amb, reference, begin_meas_states, code_meas, phas_meas, laser_meas, pseudo_meas, orbit_meas, filter_meas, end_meas_states, begin_orbit_states, orbit, cr, cd, emp_d_0, emp_d_1, emp_d_2, emp_d_3, emp_d_4, emp_y_0, emp_y_1, emp_y_2, emp_y_3, emp_y_4, emp_b_0, emp_b_1, emp_b_2, emp_b_3, emp_b_4, emp_r_0, emp_r_1, emp_r_2, emp_r_3, emp_r_4, emp_t_0, emp_t_1, emp_t_2, emp_t_3, emp_t_4, emp_n_0, emp_n_1, emp_n_2, emp_n_3, emp_n_4, emp_p_0, emp_p_1, emp_p_2, emp_p_3, emp_p_4, emp_q_0, emp_q_1, emp_q_2, emp_q_3, emp_q_4, end_orbit_states, begin_inertial_states, gyro_bias, gyro_scale, accl_bias, accl_scale, imu_offset, end_inertial_states, range]
rts: ⯆ # RTS allows reverse smoothing of estimates such that early estimates can make use of later data.
interval: # Number of seconds to use between fixed lag in RTS smoothing.
enable: # Perform backward smoothing of states to improve precision of earlier states
lag: # Number of epochs to use in RTS smoothing. Negative numbers indicate full reverse smoothing.
directory: # Directory for rts intermediate files
filename: # Base filename for rts intermediate files
inverter: # Inverter to be used within the rts processor, which may provide different performance outcomes in terms of processing time and accuracy and stability. {none, inv, llt, ldlt, colpivhqr, bdcsvd, jacobisvd, fullpivlu, first_unsupported, fullpivhqr}
queue_outputs: # Queue rts outputs so that processing is not limited by IO bandwidth
suffix: # Suffix to be applied to smoothed versions of files
simulate_filter_only: # Residuals will be calculated, but no adjustments to state or covariances will be applied
minimum_constraints: ⯆ # Receiver coodinates may be aligned to reference frames with minimal external constraints
outlier_screening: ⯆ # Statistical checks allow for detection of outliers that exceed their confidence intervals.
chi_square: ⯆
enable: # Enable Chi-square test
mode: # Chi-square test mode {innovation, measurement, state}
sigma_threshold: # Chi-square test threshold in terms of 'times of sigma'
postfit: ⯆
max_iterations: # Maximum number of measurements to exclude using postfit checks while iterating filter
meas_sigma_threshold: # Sigma threshold for measurements
sigma_check: # Enable sigma check
sigma_threshold: # Sigma threshold
state_sigma_threshold: # Sigma threshold for states
prefit: ⯆
max_iterations: # Maximum number of measurements to exclude using prefit checks before attempting to filter
meas_sigma_threshold: # Sigma threshold for measurements
omega_test: # Enable omega-test
sigma_check: # Enable sigma check
sigma_threshold: # Sigma threshold
state_sigma_threshold: # Sigma threshold for states
advanced_postfits: # Use alternate calculation method to determine postfit residuals
enable: # Transform states by minimal constraints to selected receiver coordinates
delay: ⯆ # Estimation and application of clock delay adjustment
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
rotation: ⯆ # Estimation and application of angular offsets
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
scale: ⯆ # Estimation and application of scaling factor
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
translation: ⯆ # Estimation and application of CoG offsets
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
application_mode: # Method of transforming positions {pseudo_obs, weight_matrix, variance_inverse, covariance_inverse}
constrain_orbits: # Enforce rigid transformations of orbital states
full_vcv: # ! experimental ! Use full VCV for measurement noise in minimum constraints filter
once_per_epoch: # Perform minimum constraints on a temporary filter and output results once per epoch
transform_unweighted: # Add design entries for transformation of positions without weighting
inverter: # Inverter to be used within the Kalman filter update stage, which may provide different performance outcomes in terms of processing time and accuracy and stability. {none, inv, llt, ldlt, colpivhqr, bdcsvd, jacobisvd, fullpivlu, first_unsupported, fullpivhqr}
joseph_stabilisation:
rts: ⯆ # RTS allows reverse smoothing of estimates such that early estimates can make use of later data.
interval: # Number of seconds to use between fixed lag in RTS smoothing.
enable: # Perform backward smoothing of states to improve precision of earlier states
lag: # Number of epochs to use in RTS smoothing. Negative numbers indicate full reverse smoothing.
directory: # Directory for rts intermediate files
filename: # Base filename for rts intermediate files
inverter: # Inverter to be used within the rts processor, which may provide different performance outcomes in terms of processing time and accuracy and stability. {none, inv, llt, ldlt, colpivhqr, bdcsvd, jacobisvd, fullpivlu, first_unsupported, fullpivhqr}
queue_outputs: # Queue rts outputs so that processing is not limited by IO bandwidth
suffix: # Suffix to be applied to smoothed versions of files
model_error_handling: ⯆ # The kalman filter is capable of automatic statistical integrity modelling
error_accumulation: ⯆ # Any receivers or satellites that are consistently getting many measurement rejections may be reinitialiased
enable: # Enable reinitialisation of receivers upon many rejections
receiver_error_count_threshold: # Number of errors for a receiver to be considered in error for a single epoch
receiver_error_epochs_threshold: # Number of consecutive epochs with receiver in error before it is removed and reinitialised
satellite_error_count_threshold: # Number of errors for a satellite to be considered in error for a single epoch
satellite_error_epochs_threshold: # Number of consecutive epochs with satellite in error before it is reinitialised using the orbit_errors configs
meas_deweighting: ⯆ # Measurements that are outside the expected confidence bounds may be deweighted so that outliers do not contaminate the filtered solution
deweight_factor: # Factor to downweight the variance of measurements with statistically detected errors
enable: # Enable deweighting of all rejected measurement
state_deweighting: ⯆ # Any "state" errors cause deweighting of all measurements that reference the state
deweight_factor: # Factor to downweight the variance of measurements with statistically detected errors
enable: # Enable deweighting of all referencing measurements
scale_by_design_entry: # Scale the deweighting by the magnitude of the design entry
ambiguities: ⯆ # Cycle slips in ambiguities are primary cause of incorrect gnss modelling and may be reinitialised
phase_reject_limit: # Maximum number of phase measurements to reject before the ambiguity associated with the measurement is reset.
reset_on: ⯆
gf: # Reset ambiguities if GF test is detecting a slip
lli: # Reset ambiguities if LLI test is detecting a slip
mw: # Reset ambiguities if MW test is detecting a slip
scdia: # Reset ambiguities if SCDIA test is detecting a slip
ionospheric_components: ⯆
outage_reset_limit: # Maximum number of seconds without measurements before the ionosphere associated with the measurement is reset.
exclusions: ⯆ # Cycle slips may be detected by the preprocessor and measurements rejected or ambiguities reinitialised
bad_spp: # Exclude measurements that were associated with failed SPP
config: # Exclude measurements that are configured as exclusions
eclipse: # Exclude measurements that are in eclipse
elevation: # Exclude measurements that fall below elevation mask
gf: # Exclude measurements that fail GF slip test in preprocessor
lli: # Exclude measurements that fail LLI slip test in preprocessor
mw: # Exclude measurements that fail MW slip test in preprocessor
outlier: # Exclude measurements that were rejected as SPP outliers
scdia: # Exclude measurements that fail SCDIA test in preprocessor
svh: # Exclude measurements that are not specified as healthy
system: # Exclude measurements that have been excluded by system configs
satellite_errors: ⯆ # Orbital states that are not consistent with measurements may be reinitialised to allow for dynamic maneuvers
clk_process_noise: # Sigma to apply to satellite clock states as reinitialisation
enable: # Enable applying process noise impulses to satellites upon state errors
pos_process_noise: # Sigma to apply to orbital position states as reinitialisation
vel_process_noise: # Sigma to apply to orbital velocity states as reinitialisation
vel_process_noise_trail: # Initial sigma for exponentially decaying noise to apply for subsequent epochs as soft reinitialisation
vel_process_noise_trail_tau: # Time constant for exponentially decauing noise
ambiguity_resolution: ⯆
elevation_mask: # Minimum satellite elevation to perform ambiguity resolution
fix_and_hold: # Perform ambiguity resolution and commit results to the main processing filter
lambda_set_size: # Maximum numer of candidate sets to be used in lambda_alt2 and lambda_bie modes
max_rounding_iterations: # Maximum number of rounding iterations performed in iter_rnd and bootst modes
mode: # {off, round, iter_rnd, bootst, lambda, lambda_alt, lambda_al2, lambda_bie}
once_per_epoch: # Perform ambiguity resolution on a temporary filter and output results once per epoch
solution_ratio_threshold: # Thresold for integer validation, distance ratio test.
success_rate_threshold: # Thresold for integer validation, success rate test.
ion_filter: ⯆ # Configurations for the ionospheric model kalman filter and its sub processes
outlier_screening: ⯆ # Statistical checks allow for detection of outliers that exceed their confidence intervals.
chi_square: ⯆
enable: # Enable Chi-square test
mode: # Chi-square test mode {innovation, measurement, state}
sigma_threshold: # Chi-square test threshold in terms of 'times of sigma'
postfit: ⯆
max_iterations: # Maximum number of measurements to exclude using postfit checks while iterating filter
meas_sigma_threshold: # Sigma threshold for measurements
sigma_check: # Enable sigma check
sigma_threshold: # Sigma threshold
state_sigma_threshold: # Sigma threshold for states
prefit: ⯆
max_iterations: # Maximum number of measurements to exclude using prefit checks before attempting to filter
meas_sigma_threshold: # Sigma threshold for measurements
omega_test: # Enable omega-test
sigma_check: # Enable sigma check
sigma_threshold: # Sigma threshold
state_sigma_threshold: # Sigma threshold for states
advanced_postfits: # Use alternate calculation method to determine postfit residuals
estimate_sat_dcb: # Estimate satellite dcb alongside Ionosphere models, should be false for local STEC
function_degree: # Maximum degree of Spherical harmonics for Ionospheric mapping
function_order: # Maximum order of Spherical harmonics for Ionospheric mapping
inverter: # Inverter to be used within the Kalman filter update stage, which may provide different performance outcomes in terms of processing time and accuracy and stability. {none, inv, llt, ldlt, colpivhqr, bdcsvd, jacobisvd, fullpivlu, first_unsupported, fullpivhqr}
joseph_stabilisation:
layer_heights: # List of heights of ionosphere layers to estimate
model: # {none, meas_out, bspline, spherical_caps, spherical_harmonics, local}
model_sigma_limit: # Ionosphere states are removed when their sigma exceeds this value
rts: ⯆ # RTS allows reverse smoothing of estimates such that early estimates can make use of later data.
interval: # Number of seconds to use between fixed lag in RTS smoothing.
enable: # Perform backward smoothing of states to improve precision of earlier states
lag: # Number of epochs to use in RTS smoothing. Negative numbers indicate full reverse smoothing.
directory: # Directory for rts intermediate files
filename: # Base filename for rts intermediate files
inverter: # Inverter to be used within the rts processor, which may provide different performance outcomes in terms of processing time and accuracy and stability. {none, inv, llt, ldlt, colpivhqr, bdcsvd, jacobisvd, fullpivlu, first_unsupported, fullpivhqr}
queue_outputs: # Queue rts outputs so that processing is not limited by IO bandwidth
suffix: # Suffix to be applied to smoothed versions of files
use_rotation_mtx: # Use 3D rotation matrix for spherical harmonics to maintain orientation toward the sun
orbit_propagation: ⯆
aod: # Model Atmospheric and Oceanic non tidal accelerations
atm_tide: # Model accelerations due to atmospheric tides model
central_force: # Acceleration due to the central force
egm_degree: # Degree of spherical harmonics gravity model
egm_field: # Acceleration due to the high degree model of the Earth gravity model (exclude degree 0, made by central_force)
general_relativity: # Model acceleration due general relativisty
indirect_j2: # J2 acceleration perturbation due to the Sun and Moon
integrator_time_step: # Timestep for the integrator, must be smaller than the processing time step, might be adjusted if the processing time step isn't a integer number of time steps
ocean_tide: # Model accelerations due to ocean tides model
pole_tide_ocean: # Model accelerations due to ocean pole tide (degree 2 only)
pole_tide_solid: # Model accelerations due to solid pole tide (degree 2 only)
solid_earth_tide: # Model accelerations due to solid earth tides
predictions: ⯆
forward_duration:
interval:
offset:
reverse_duration:
duration_units: # {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
interval_units: # {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
user_aliases: # User definable alias pairs, eg 'myuser:mypass' (use quotes) will replace instances of with mypass in relevant filenames
receiver_options: ⯆ # Options to configure individual satellites, systems, or global configs
global: ⯆
elevation_mask: # Minimum elevation for satellites to be processed
exclude: # Exclude receiver from processing
kill: # Remove receiver from future processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
zero_dcb_codes: # [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
antenna_type: # Antenna type and radome in 20 character string as per sinex
apriori_position: # Apriori position in XYZ ECEF frame
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
receiver_type: # Type of gnss receiver hardware
sat_id: # Id for receivers that are also satellites
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
eccentricity: ⯆
enable: # Enable antenna eccentrities
offset: # Antenna offset in ENU frame
eop: ⯆
enable: # Enable modelling of eops
integer_ambiguity: ⯆
enable: # Model ambiguities due to unknown integer number of cycles in phase measurements
ionospheric_components: ⯆ # Ionospheric models produce frequency-dependent effects
geomagnetic_field_height: # ionospheric pierce point layer height if not specified in the data or model (km)
mapping_function: # Mapping function if not specified in the data or model {slm, mslm, mlm, klobuchar}
mapping_function_layer_height: # mapping function layer height if not specified in the data or model (km)
enable: # Enable ionospheric modelling
use_2nd_order:
use_3rd_order:
ionospheric_model: ⯆ # Coherent ionosphere models can improve estimation of biases and allow use with single frequency receivers
enable: # Compute ionosphere maps from a network of receivers
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
range: ⯆
enable: # Enable modelling of signal time of flight time due to range
relativity2: ⯆
enable: # Enable modelling of secondary relativistic effects
relativity: ⯆
enable: # Enable modelling of relativistic effects
sagnac: ⯆
enable: # Enable modelling of sagnac effect
tides: ⯆
atl: # Enable atmospheric tide loading
enable: # Enable modelling of tidal displacements
opole: # Enable ocean pole tides
otl: # Enable ocean tide loading
solid: # Enable solid Earth tides
spole: # Enable solid Earth pole tides
troposphere: ⯆ # Tropospheric modelling accounts for delays due to refraction of light in water vapour
enable: # Model tropospheric delays
models: # List of models to use for troposphere [standard, sbas, vmf3, gpt2, cssr]
tropospheric_map: ⯆
enable: # Compute tropospheric maps from a network of receivers
aliases: # Aliases for this receiver
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
rec_reference_system: # Receiver will use this system as reference clock {none, gps, gal, glo, qzs, sbs, bds, leo, supported, irn, ims, comb}
rinex2: ⯆
rnx_code_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
rnx_phase_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
gps: ⯆
elevation_mask: # Minimum elevation for satellites to be processed
exclude: # Exclude receiver from processing
kill: # Remove receiver from future processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
zero_dcb_codes: # [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
antenna_type: # Antenna type and radome in 20 character string as per sinex
apriori_position: # Apriori position in XYZ ECEF frame
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
receiver_type: # Type of gnss receiver hardware
sat_id: # Id for receivers that are also satellites
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
eccentricity: ⯆
enable: # Enable antenna eccentrities
offset: # Antenna offset in ENU frame
eop: ⯆
enable: # Enable modelling of eops
integer_ambiguity: ⯆
enable: # Model ambiguities due to unknown integer number of cycles in phase measurements
ionospheric_components: ⯆ # Ionospheric models produce frequency-dependent effects
geomagnetic_field_height: # ionospheric pierce point layer height if not specified in the data or model (km)
mapping_function: # Mapping function if not specified in the data or model {slm, mslm, mlm, klobuchar}
mapping_function_layer_height: # mapping function layer height if not specified in the data or model (km)
enable: # Enable ionospheric modelling
use_2nd_order:
use_3rd_order:
ionospheric_model: ⯆ # Coherent ionosphere models can improve estimation of biases and allow use with single frequency receivers
enable: # Compute ionosphere maps from a network of receivers
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
range: ⯆
enable: # Enable modelling of signal time of flight time due to range
relativity2: ⯆
enable: # Enable modelling of secondary relativistic effects
relativity: ⯆
enable: # Enable modelling of relativistic effects
sagnac: ⯆
enable: # Enable modelling of sagnac effect
tides: ⯆
atl: # Enable atmospheric tide loading
enable: # Enable modelling of tidal displacements
opole: # Enable ocean pole tides
otl: # Enable ocean tide loading
solid: # Enable solid Earth tides
spole: # Enable solid Earth pole tides
troposphere: ⯆ # Tropospheric modelling accounts for delays due to refraction of light in water vapour
enable: # Model tropospheric delays
models: # List of models to use for troposphere [standard, sbas, vmf3, gpt2, cssr]
tropospheric_map: ⯆
enable: # Compute tropospheric maps from a network of receivers
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
l1w: ⯆
elevation_mask: # Minimum elevation for satellites to be processed
exclude: # Exclude receiver from processing
kill: # Remove receiver from future processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
zero_dcb_codes: # [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
antenna_type: # Antenna type and radome in 20 character string as per sinex
apriori_position: # Apriori position in XYZ ECEF frame
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
receiver_type: # Type of gnss receiver hardware
sat_id: # Id for receivers that are also satellites
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
eccentricity: ⯆
enable: # Enable antenna eccentrities
offset: # Antenna offset in ENU frame
eop: ⯆
enable: # Enable modelling of eops
integer_ambiguity: ⯆
enable: # Model ambiguities due to unknown integer number of cycles in phase measurements
ionospheric_components: ⯆ # Ionospheric models produce frequency-dependent effects
geomagnetic_field_height: # ionospheric pierce point layer height if not specified in the data or model (km)
mapping_function: # Mapping function if not specified in the data or model {slm, mslm, mlm, klobuchar}
mapping_function_layer_height: # mapping function layer height if not specified in the data or model (km)
enable: # Enable ionospheric modelling
use_2nd_order:
use_3rd_order:
ionospheric_model: ⯆ # Coherent ionosphere models can improve estimation of biases and allow use with single frequency receivers
enable: # Compute ionosphere maps from a network of receivers
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
range: ⯆
enable: # Enable modelling of signal time of flight time due to range
relativity2: ⯆
enable: # Enable modelling of secondary relativistic effects
relativity: ⯆
enable: # Enable modelling of relativistic effects
sagnac: ⯆
enable: # Enable modelling of sagnac effect
tides: ⯆
atl: # Enable atmospheric tide loading
enable: # Enable modelling of tidal displacements
opole: # Enable ocean pole tides
otl: # Enable ocean tide loading
solid: # Enable solid Earth tides
spole: # Enable solid Earth pole tides
troposphere: ⯆ # Tropospheric modelling accounts for delays due to refraction of light in water vapour
enable: # Model tropospheric delays
models: # List of models to use for troposphere [standard, sbas, vmf3, gpt2, cssr]
tropospheric_map: ⯆
enable: # Compute tropospheric maps from a network of receivers
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
rec_reference_system: # Receiver will use this system as reference clock {none, gps, gal, glo, qzs, sbs, bds, leo, supported, irn, ims, comb}
rinex2: ⯆
rnx_code_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
rnx_phase_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
rec_reference_system: # Receiver will use this system as reference clock {none, gps, gal, glo, qzs, sbs, bds, leo, supported, irn, ims, comb}
rinex2: ⯆
rnx_code_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
rnx_phase_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
xmpl: ⯆
elevation_mask: # Minimum elevation for satellites to be processed
exclude: # Exclude receiver from processing
kill: # Remove receiver from future processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
zero_dcb_codes: # [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
antenna_type: # Antenna type and radome in 20 character string as per sinex
apriori_position: # Apriori position in XYZ ECEF frame
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
receiver_type: # Type of gnss receiver hardware
sat_id: # Id for receivers that are also satellites
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
eccentricity: ⯆
enable: # Enable antenna eccentrities
offset: # Antenna offset in ENU frame
eop: ⯆
enable: # Enable modelling of eops
integer_ambiguity: ⯆
enable: # Model ambiguities due to unknown integer number of cycles in phase measurements
ionospheric_components: ⯆ # Ionospheric models produce frequency-dependent effects
geomagnetic_field_height: # ionospheric pierce point layer height if not specified in the data or model (km)
mapping_function: # Mapping function if not specified in the data or model {slm, mslm, mlm, klobuchar}
mapping_function_layer_height: # mapping function layer height if not specified in the data or model (km)
enable: # Enable ionospheric modelling
use_2nd_order:
use_3rd_order:
ionospheric_model: ⯆ # Coherent ionosphere models can improve estimation of biases and allow use with single frequency receivers
enable: # Compute ionosphere maps from a network of receivers
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
range: ⯆
enable: # Enable modelling of signal time of flight time due to range
relativity2: ⯆
enable: # Enable modelling of secondary relativistic effects
relativity: ⯆
enable: # Enable modelling of relativistic effects
sagnac: ⯆
enable: # Enable modelling of sagnac effect
tides: ⯆
atl: # Enable atmospheric tide loading
enable: # Enable modelling of tidal displacements
opole: # Enable ocean pole tides
otl: # Enable ocean tide loading
solid: # Enable solid Earth tides
spole: # Enable solid Earth pole tides
troposphere: ⯆ # Tropospheric modelling accounts for delays due to refraction of light in water vapour
enable: # Model tropospheric delays
models: # List of models to use for troposphere [standard, sbas, vmf3, gpt2, cssr]
tropospheric_map: ⯆
enable: # Compute tropospheric maps from a network of receivers
aliases: # Aliases for this receiver
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
rec_reference_system: # Receiver will use this system as reference clock {none, gps, gal, glo, qzs, sbs, bds, leo, supported, irn, ims, comb}
rinex2: ⯆
rnx_code_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
rnx_phase_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
gps: ⯆
elevation_mask: # Minimum elevation for satellites to be processed
exclude: # Exclude receiver from processing
kill: # Remove receiver from future processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
zero_dcb_codes: # [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
antenna_type: # Antenna type and radome in 20 character string as per sinex
apriori_position: # Apriori position in XYZ ECEF frame
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
receiver_type: # Type of gnss receiver hardware
sat_id: # Id for receivers that are also satellites
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
eccentricity: ⯆
enable: # Enable antenna eccentrities
offset: # Antenna offset in ENU frame
eop: ⯆
enable: # Enable modelling of eops
integer_ambiguity: ⯆
enable: # Model ambiguities due to unknown integer number of cycles in phase measurements
ionospheric_components: ⯆ # Ionospheric models produce frequency-dependent effects
geomagnetic_field_height: # ionospheric pierce point layer height if not specified in the data or model (km)
mapping_function: # Mapping function if not specified in the data or model {slm, mslm, mlm, klobuchar}
mapping_function_layer_height: # mapping function layer height if not specified in the data or model (km)
enable: # Enable ionospheric modelling
use_2nd_order:
use_3rd_order:
ionospheric_model: ⯆ # Coherent ionosphere models can improve estimation of biases and allow use with single frequency receivers
enable: # Compute ionosphere maps from a network of receivers
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
range: ⯆
enable: # Enable modelling of signal time of flight time due to range
relativity2: ⯆
enable: # Enable modelling of secondary relativistic effects
relativity: ⯆
enable: # Enable modelling of relativistic effects
sagnac: ⯆
enable: # Enable modelling of sagnac effect
tides: ⯆
atl: # Enable atmospheric tide loading
enable: # Enable modelling of tidal displacements
opole: # Enable ocean pole tides
otl: # Enable ocean tide loading
solid: # Enable solid Earth tides
spole: # Enable solid Earth pole tides
troposphere: ⯆ # Tropospheric modelling accounts for delays due to refraction of light in water vapour
enable: # Model tropospheric delays
models: # List of models to use for troposphere [standard, sbas, vmf3, gpt2, cssr]
tropospheric_map: ⯆
enable: # Compute tropospheric maps from a network of receivers
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
l1w: ⯆
elevation_mask: # Minimum elevation for satellites to be processed
exclude: # Exclude receiver from processing
kill: # Remove receiver from future processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
zero_dcb_codes: # [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
antenna_type: # Antenna type and radome in 20 character string as per sinex
apriori_position: # Apriori position in XYZ ECEF frame
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
receiver_type: # Type of gnss receiver hardware
sat_id: # Id for receivers that are also satellites
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
eccentricity: ⯆
enable: # Enable antenna eccentrities
offset: # Antenna offset in ENU frame
eop: ⯆
enable: # Enable modelling of eops
integer_ambiguity: ⯆
enable: # Model ambiguities due to unknown integer number of cycles in phase measurements
ionospheric_components: ⯆ # Ionospheric models produce frequency-dependent effects
geomagnetic_field_height: # ionospheric pierce point layer height if not specified in the data or model (km)
mapping_function: # Mapping function if not specified in the data or model {slm, mslm, mlm, klobuchar}
mapping_function_layer_height: # mapping function layer height if not specified in the data or model (km)
enable: # Enable ionospheric modelling
use_2nd_order:
use_3rd_order:
ionospheric_model: ⯆ # Coherent ionosphere models can improve estimation of biases and allow use with single frequency receivers
enable: # Compute ionosphere maps from a network of receivers
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
range: ⯆
enable: # Enable modelling of signal time of flight time due to range
relativity2: ⯆
enable: # Enable modelling of secondary relativistic effects
relativity: ⯆
enable: # Enable modelling of relativistic effects
sagnac: ⯆
enable: # Enable modelling of sagnac effect
tides: ⯆
atl: # Enable atmospheric tide loading
enable: # Enable modelling of tidal displacements
opole: # Enable ocean pole tides
otl: # Enable ocean tide loading
solid: # Enable solid Earth tides
spole: # Enable solid Earth pole tides
troposphere: ⯆ # Tropospheric modelling accounts for delays due to refraction of light in water vapour
enable: # Model tropospheric delays
models: # List of models to use for troposphere [standard, sbas, vmf3, gpt2, cssr]
tropospheric_map: ⯆
enable: # Compute tropospheric maps from a network of receivers
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
rec_reference_system: # Receiver will use this system as reference clock {none, gps, gal, glo, qzs, sbs, bds, leo, supported, irn, ims, comb}
rinex2: ⯆
rnx_code_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
rnx_phase_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
rec_reference_system: # Receiver will use this system as reference clock {none, gps, gal, glo, qzs, sbs, bds, leo, supported, irn, ims, comb}
rinex2: ⯆
rnx_code_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
rnx_phase_conversions: ⯆
c1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
c8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l3: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l4: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l5: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l6: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l7: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
l8: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
la: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
none: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p1: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
p2: # {none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto}
satellite_options: ⯆
global: ⯆
exclude: # Exclude receiver from processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
surface_details: # List of details for srp and drag surfaces
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
l1w: ⯆
exclude: # Exclude receiver from processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
surface_details: # List of details for srp and drag surfaces
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
orbit_propagation: ⯆ # Enable specific orbit propagation models
area: # Satellite area for use in solar radiation and albedo calculations
mass: # Satellite mass for use if not specified in the SINEX metadata file
drag_cd: # Coefficient of drag of the satellite
power: # Transmission power use if not specified in the SINEX metadata file
srp_cr: # Coefficient of reflection of the satellite
albedo: # Model accelerations due to the albedo effect from Earth (Visible and Infra-red) {none, cannonball, boxwing}
antenna_thrust: # Model accelerations due to the emitted signal from the antenna
drag: # Model accelerations due to drag
empirical: # Model accelerations due to empirical accelerations
empirical_dyb_eclipse: # Turn on/off the eclipse on each axis (D, Y, B)
empirical_rtn_eclipse: # Turn on/off the eclipse on each axis (R, T, N)
planetary_perturbations: # Acceleration due to third celestial bodies [mercury, venus, earth, mars, jupiter, saturn, uranus, neptune, pluto, moon, sun]
pseudo_pulses: ⯆ # Apply process noise to simulate pseudo-stochastic pulses commonly applied in least squares solutions
enable: # Enable applying process noise impulses to orbits upon state errors
interval: # Interval between applying pseudo pulses
pos_process_noise: # Sigma to add to orbital position states
vel_process_noise: # Sigma to add to orbital velocity states
solar_radiation_pressure: # Model accelerations due to solar radiation pressure {none, cannonball, boxwing}
orbit_propagation: ⯆ # Enable specific orbit propagation models
area: # Satellite area for use in solar radiation and albedo calculations
mass: # Satellite mass for use if not specified in the SINEX metadata file
drag_cd: # Coefficient of drag of the satellite
power: # Transmission power use if not specified in the SINEX metadata file
srp_cr: # Coefficient of reflection of the satellite
albedo: # Model accelerations due to the albedo effect from Earth (Visible and Infra-red) {none, cannonball, boxwing}
antenna_thrust: # Model accelerations due to the emitted signal from the antenna
drag: # Model accelerations due to drag
empirical: # Model accelerations due to empirical accelerations
empirical_dyb_eclipse: # Turn on/off the eclipse on each axis (D, Y, B)
empirical_rtn_eclipse: # Turn on/off the eclipse on each axis (R, T, N)
planetary_perturbations: # Acceleration due to third celestial bodies [mercury, venus, earth, mars, jupiter, saturn, uranus, neptune, pluto, moon, sun]
pseudo_pulses: ⯆ # Apply process noise to simulate pseudo-stochastic pulses commonly applied in least squares solutions
enable: # Enable applying process noise impulses to orbits upon state errors
interval: # Interval between applying pseudo pulses
pos_process_noise: # Sigma to add to orbital position states
vel_process_noise: # Sigma to add to orbital velocity states
solar_radiation_pressure: # Model accelerations due to solar radiation pressure {none, cannonball, boxwing}
g--: ⯆
exclude: # Exclude receiver from processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
surface_details: # List of details for srp and drag surfaces
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
aliases: # Aliases for this satellite
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
l1w: ⯆
exclude: # Exclude receiver from processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
surface_details: # List of details for srp and drag surfaces
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
orbit_propagation: ⯆ # Enable specific orbit propagation models
area: # Satellite area for use in solar radiation and albedo calculations
mass: # Satellite mass for use if not specified in the SINEX metadata file
drag_cd: # Coefficient of drag of the satellite
power: # Transmission power use if not specified in the SINEX metadata file
srp_cr: # Coefficient of reflection of the satellite
albedo: # Model accelerations due to the albedo effect from Earth (Visible and Infra-red) {none, cannonball, boxwing}
antenna_thrust: # Model accelerations due to the emitted signal from the antenna
drag: # Model accelerations due to drag
empirical: # Model accelerations due to empirical accelerations
empirical_dyb_eclipse: # Turn on/off the eclipse on each axis (D, Y, B)
empirical_rtn_eclipse: # Turn on/off the eclipse on each axis (R, T, N)
planetary_perturbations: # Acceleration due to third celestial bodies [mercury, venus, earth, mars, jupiter, saturn, uranus, neptune, pluto, moon, sun]
pseudo_pulses: ⯆ # Apply process noise to simulate pseudo-stochastic pulses commonly applied in least squares solutions
enable: # Enable applying process noise impulses to orbits upon state errors
interval: # Interval between applying pseudo pulses
pos_process_noise: # Sigma to add to orbital position states
vel_process_noise: # Sigma to add to orbital velocity states
solar_radiation_pressure: # Model accelerations due to solar radiation pressure {none, cannonball, boxwing}
orbit_propagation: ⯆ # Enable specific orbit propagation models
area: # Satellite area for use in solar radiation and albedo calculations
mass: # Satellite mass for use if not specified in the SINEX metadata file
drag_cd: # Coefficient of drag of the satellite
power: # Transmission power use if not specified in the SINEX metadata file
srp_cr: # Coefficient of reflection of the satellite
albedo: # Model accelerations due to the albedo effect from Earth (Visible and Infra-red) {none, cannonball, boxwing}
antenna_thrust: # Model accelerations due to the emitted signal from the antenna
drag: # Model accelerations due to drag
empirical: # Model accelerations due to empirical accelerations
empirical_dyb_eclipse: # Turn on/off the eclipse on each axis (D, Y, B)
empirical_rtn_eclipse: # Turn on/off the eclipse on each axis (R, T, N)
planetary_perturbations: # Acceleration due to third celestial bodies [mercury, venus, earth, mars, jupiter, saturn, uranus, neptune, pluto, moon, sun]
pseudo_pulses: ⯆ # Apply process noise to simulate pseudo-stochastic pulses commonly applied in least squares solutions
enable: # Enable applying process noise impulses to orbits upon state errors
interval: # Interval between applying pseudo pulses
pos_process_noise: # Sigma to add to orbital position states
vel_process_noise: # Sigma to add to orbital velocity states
solar_radiation_pressure: # Model accelerations due to solar radiation pressure {none, cannonball, boxwing}
gps: ⯆
exclude: # Exclude receiver from processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
surface_details: # List of details for srp and drag surfaces
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
l1w: ⯆
exclude: # Exclude receiver from processing
laser_sigma: # Standard deviation of SLR laser measurements
pseudo_sigma: # Standard deviation of pseudo measurmeents
error_model: # {uniform, elevation_dependent}
code_sigma: # Standard deviation of code measurements
phase_sigma: # Standard deviation of phase measurmeents
clock_codes: # Codes for IF combination based clocks [none, l1c, l1p, l1w, l1y, l1m, l1n, l1s, l1l, l1e, l1a, l1b, l1x, l1z, l2c, l2d, l2s, l2l, l2x, l2p, l2w, l2y, l2m, l2n, l5i, l5q, l5x, l7i, l7q, l7x, l6a, l6b, l6c, l6x, l6z, l6s, l6l, l8i, l8q, l8x, l2i, l2q, l6i, l6q, l3i, l3q, l3x, l1i, l1q, l4a, l4b, l4x, l6e, l1d, l5d, l5p, l9a, l9b, l9c, l9x, l5a, l5b, l5c, l5z, l6d, l6p, l7d, l7p, l7z, l8d, l8p, auto]
apriori_sigma_enu: # Sigma applied for weighting in mincon transformation estimation. (Lower is stronger weighting, Negative is unweighted, ENU separation unsupported for satellites)
mincon_scale_apriori_sigma: # Scale applied to apriori sigmas while weighting in mincon transformation estimation
mincon_scale_filter_sigma: # Scale applied to filter sigmas while weighting in mincon transformation estimation
surface_details: # List of details for srp and drag surfaces
models: ⯆ # Enable specific models
attitude: ⯆
enable: # Enables non-nominal attitude types
model_dt: # Timestep used in modelling attitude
sources: # List of sourecs to use for attitudes [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
clock: ⯆
enable: # Enable modelling of clocks
sources: # List of sources to use for clocks [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
code_bias: ⯆
default_bias: # Bias to use when no code bias is found
enable: # Enable modelling of code biases
undefined_sigma: # Uncertainty sigma to apply to default code biases
pco: ⯆
enable: # Enable modelling of phase center offsets
pcv: ⯆
enable: # Enable modelling of phase center variations
phase_bias: ⯆
default_bias: # Bias to use when no phase bias is found
enable: # Enable modelling of phase biases. Required for AR
undefined_sigma: # Uncertainty sigma to apply to default phase biases
phase_windup: ⯆
enable: # Model phase windup due to relative rotation of circularly polarised antennas
pos: ⯆
enable: # Enable modelling of position
sources: # Enable modelling of position [none, spp, config, precise, ssr, kalman, broadcast, nominal, model, pseudo, remote]
antenna_azimuth: # Antenna azimuth (North) in satellite body-fixed frame
antenna_boresight: # Antenna boresight (Up) in satellite body-fixed frame
ellipse_propagation_time_tolerance: # Time gap tolerance under which the ellipse propagator can be used for orbit prediction
orbit_propagation: ⯆ # Enable specific orbit propagation models
area: # Satellite area for use in solar radiation and albedo calculations
mass: # Satellite mass for use if not specified in the SINEX metadata file
drag_cd: # Coefficient of drag of the satellite
power: # Transmission power use if not specified in the SINEX metadata file
srp_cr: # Coefficient of reflection of the satellite
albedo: # Model accelerations due to the albedo effect from Earth (Visible and Infra-red) {none, cannonball, boxwing}
antenna_thrust: # Model accelerations due to the emitted signal from the antenna
drag: # Model accelerations due to drag
empirical: # Model accelerations due to empirical accelerations
empirical_dyb_eclipse: # Turn on/off the eclipse on each axis (D, Y, B)
empirical_rtn_eclipse: # Turn on/off the eclipse on each axis (R, T, N)
planetary_perturbations: # Acceleration due to third celestial bodies [mercury, venus, earth, mars, jupiter, saturn, uranus, neptune, pluto, moon, sun]
pseudo_pulses: ⯆ # Apply process noise to simulate pseudo-stochastic pulses commonly applied in least squares solutions
enable: # Enable applying process noise impulses to orbits upon state errors
interval: # Interval between applying pseudo pulses
pos_process_noise: # Sigma to add to orbital position states
vel_process_noise: # Sigma to add to orbital velocity states
solar_radiation_pressure: # Model accelerations due to solar radiation pressure {none, cannonball, boxwing}
orbit_propagation: ⯆ # Enable specific orbit propagation models
area: # Satellite area for use in solar radiation and albedo calculations
mass: # Satellite mass for use if not specified in the SINEX metadata file
drag_cd: # Coefficient of drag of the satellite
power: # Transmission power use if not specified in the SINEX metadata file
srp_cr: # Coefficient of reflection of the satellite
albedo: # Model accelerations due to the albedo effect from Earth (Visible and Infra-red) {none, cannonball, boxwing}
antenna_thrust: # Model accelerations due to the emitted signal from the antenna
drag: # Model accelerations due to drag
empirical: # Model accelerations due to empirical accelerations
empirical_dyb_eclipse: # Turn on/off the eclipse on each axis (D, Y, B)
empirical_rtn_eclipse: # Turn on/off the eclipse on each axis (R, T, N)
planetary_perturbations: # Acceleration due to third celestial bodies [mercury, venus, earth, mars, jupiter, saturn, uranus, neptune, pluto, moon, sun]
pseudo_pulses: ⯆ # Apply process noise to simulate pseudo-stochastic pulses commonly applied in least squares solutions
enable: # Enable applying process noise impulses to orbits upon state errors
interval: # Interval between applying pseudo pulses
pos_process_noise: # Sigma to add to orbital position states
vel_process_noise: # Sigma to add to orbital velocity states
solar_radiation_pressure: # Model accelerations due to solar radiation pressure {none, cannonball, boxwing}
estimation_parameters: ⯆
receivers: ⯆
global: ⯆
ambiguities: ⯆ # Integer phase ambiguities
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_stec: ⯆ # Ionospheric slant delay
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop: ⯆ # Troposphere corrections
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_grads: ⯆ # Troposphere gradients
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_maps: ⯆ # Troposphere ZWD mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pcv: ⯆ # Antenna phase center variations (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_model: ⯆ # Ionospheric mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
strain_rate: ⯆ # Velocity (large gain, for geodetic timescales)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_range_bias: ⯆ # Satellite Laser Ranging range bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_time_bias: ⯆ # Satellite Laser Ranging time bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gps: ⯆
ambiguities: ⯆ # Integer phase ambiguities
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_stec: ⯆ # Ionospheric slant delay
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop: ⯆ # Troposphere corrections
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_grads: ⯆ # Troposphere gradients
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_maps: ⯆ # Troposphere ZWD mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pcv: ⯆ # Antenna phase center variations (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_model: ⯆ # Ionospheric mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
strain_rate: ⯆ # Velocity (large gain, for geodetic timescales)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_range_bias: ⯆ # Satellite Laser Ranging range bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_time_bias: ⯆ # Satellite Laser Ranging time bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
l1w: ⯆
ambiguities: ⯆ # Integer phase ambiguities
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_stec: ⯆ # Ionospheric slant delay
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop: ⯆ # Troposphere corrections
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_grads: ⯆ # Troposphere gradients
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_maps: ⯆ # Troposphere ZWD mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pcv: ⯆ # Antenna phase center variations (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_model: ⯆ # Ionospheric mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
strain_rate: ⯆ # Velocity (large gain, for geodetic timescales)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_range_bias: ⯆ # Satellite Laser Ranging range bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_time_bias: ⯆ # Satellite Laser Ranging time bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
xmpl: ⯆
ambiguities: ⯆ # Integer phase ambiguities
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_stec: ⯆ # Ionospheric slant delay
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop: ⯆ # Troposphere corrections
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_grads: ⯆ # Troposphere gradients
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_maps: ⯆ # Troposphere ZWD mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pcv: ⯆ # Antenna phase center variations (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_model: ⯆ # Ionospheric mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
strain_rate: ⯆ # Velocity (large gain, for geodetic timescales)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_range_bias: ⯆ # Satellite Laser Ranging range bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_time_bias: ⯆ # Satellite Laser Ranging time bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gps: ⯆
ambiguities: ⯆ # Integer phase ambiguities
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_stec: ⯆ # Ionospheric slant delay
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop: ⯆ # Troposphere corrections
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_grads: ⯆ # Troposphere gradients
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_maps: ⯆ # Troposphere ZWD mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pcv: ⯆ # Antenna phase center variations (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_model: ⯆ # Ionospheric mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
strain_rate: ⯆ # Velocity (large gain, for geodetic timescales)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_range_bias: ⯆ # Satellite Laser Ranging range bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_time_bias: ⯆ # Satellite Laser Ranging time bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
l1w: ⯆
ambiguities: ⯆ # Integer phase ambiguities
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_stec: ⯆ # Ionospheric slant delay
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop: ⯆ # Troposphere corrections
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_grads: ⯆ # Troposphere gradients
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
trop_maps: ⯆ # Troposphere ZWD mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pcv: ⯆ # Antenna phase center variations (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion_model: ⯆ # Ionospheric mapping
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
strain_rate: ⯆ # Velocity (large gain, for geodetic timescales)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_range_bias: ⯆ # Satellite Laser Ranging range bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
slr_time_bias: ⯆ # Satellite Laser Ranging time bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
satellites: ⯆
global: ⯆
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
l1w: ⯆
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
g--: ⯆
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
l1w: ⯆
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gps: ⯆
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
l1w: ⯆
clock: ⯆ # Clocks
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos: ⯆ # Position
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pos_rate: ⯆ # Velocity
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
clock_rate: ⯆ # Clock rates
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orbit: ⯆ # Orbital state
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
pco: ⯆ # Phase Center Offsets (experimental)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ant_delta: ⯆ # Antenna delta (body frame)
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
code_bias: ⯆ # Code bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
phase_bias: ⯆ # Phase bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_0: ⯆ # Empirical accleration B bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_1: ⯆ # Empirical accleration B 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_2: ⯆ # Empirical accleration B 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_3: ⯆ # Empirical accleration B 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_b_4: ⯆ # Empirical accleration B 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_0: ⯆ # Empirical accleration direct bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_1: ⯆ # Empirical accleration direct 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_2: ⯆ # Empirical accleration direct 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_3: ⯆ # Empirical accleration direct 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_d_4: ⯆ # Empirical accleration direct 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_0: ⯆ # Empirical accleration normal bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_1: ⯆ # Empirical accleration normal 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_2: ⯆ # Empirical accleration normal 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_3: ⯆ # Empirical accleration normal 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_n_4: ⯆ # Empirical accleration normal 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_0: ⯆ # Empirical accleration P bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_1: ⯆ # Empirical accleration P 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_2: ⯆ # Empirical accleration P 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_3: ⯆ # Empirical accleration P 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_p_4: ⯆ # Empirical accleration P 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_0: ⯆ # Empirical accleration Q bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_1: ⯆ # Empirical accleration Q 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_2: ⯆ # Empirical accleration Q 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_3: ⯆ # Empirical accleration Q 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_q_4: ⯆ # Empirical accleration Q 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_0: ⯆ # Empirical accleration radial bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_1: ⯆ # Empirical accleration radial 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_2: ⯆ # Empirical accleration radial 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_3: ⯆ # Empirical accleration radial 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_r_4: ⯆ # Empirical accleration radial 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_0: ⯆ # Empirical accleration tangential bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_1: ⯆ # Empirical accleration tangential 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_2: ⯆ # Empirical accleration tangential 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_3: ⯆ # Empirical accleration tangential 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_t_4: ⯆ # Empirical accleration tangential 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_0: ⯆ # Empirical accleration Y bias
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_1: ⯆ # Empirical accleration Y 1 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_2: ⯆ # Empirical accleration Y 2 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_3: ⯆ # Empirical accleration Y 3 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
emp_y_4: ⯆ # Empirical accleration Y 4 per rev
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
accelerometer_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_bias: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
gyro_scale: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
imu_offset: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
orientation: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
global_models: ⯆
eop: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
eop_rates: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
ion: ⯆
estimated: # Estimate state in kalman filter
sigma: # Apriori sigma values - if zero, will be initialised using least squares
process_noise: # Process noise sigmas
process_noise_dt: # Time unit for process noise {second, minute, hour, day, week, year, seconds, minutes, hours, days, weeks, years, sec, min, hr, dy, wk, yr, secs, mins, hrs, dys, wks, yrs, sqrt_sec, sqrt_min, sqrt_hr, sqrt_dy, sqrt_wk, sqrt_yr, sqrt_secs, sqrt_mins, sqrt_hrs, sqrt_dys, sqrt_wks, sqrt_yrs, sqrt_second, sqrt_minute, sqrt_hour, sqrt_day, sqrt_week, sqrt_year, sqrt_seconds, sqrt_minutes, sqrt_hours, sqrt_days, sqrt_weeks, sqrt_years}
apriori_value: # Apriori state values
use_remote_sigma: # Use remote filter sigma for initial sigma
comment: # Comment to apply to the state
mu: # Desired mean value for gauss markov states
outage_limit: # Maximum unestimated time before the state is removed
sigma_limit: # Maximum sigma before the state is removed
tau: # Correlation times for gauss markov noise, defaults to -1 -> inf (Random Walk)
mongo: ⯆ # Mongo is a database used to store results and intermediate values for later analysis and inter-process communication
enable: # Enable and connect to mongo database {none, primary, secondary, both}
delete_history: # Drop the collection in the database at the beginning of the run to only show fresh data {none, primary, secondary, both}
output_components: # Output components of measurements {none, primary, secondary, both}
output_cumulative: # Output cumulative residuals of components of measurements {none, primary, secondary, both}
output_measurements: # Output measurements and their residuals {none, primary, secondary, both}
output_state_covars: # Output covariance values of related states {none, primary, secondary, both}
output_states: # Output states {none, primary, secondary, both}
cull_history: # Erase old database objects to limit the size and speed degredation over long runs {none, primary, secondary, both}
min_cull_age: # Age of which to cull history
output_config: # Output config {none, primary, secondary, both}
output_logs: # Output console trace and warnings to mongo with timestamps and other metadata {none, primary, secondary, both}
output_predictions: # {none, primary, secondary, both}
output_ssr_precursors: # Output orbits, clocks, and bias estimates to allow communication to ssr generating processes {none, primary, secondary, both}
output_test_stats: # Output test statistics {none, primary, secondary, both}
output_trace: # Output trace {none, primary, secondary, both}
queue_outputs: # Output data in a separate thread - may reduce latency
use_predictions: # {none, primary, secondary, both}
primary_database:
primary_suffix: # Suffix to append to database elements to make distinctions between runs for comparison
primary_uri: # Location and port of the mongo database to connect to
secondary_database:
secondary_suffix: # Suffix to append to database elements to make distinctions between runs for comparison
secondary_uri: # Location and port of the mongo database to connect to
sent_predictions: # Filter states to predict and send to mongo [none, one, all, rec_pos, rec_vel, rec_pos_rate, rec_acc, strain_rate, pos, vel, acc, heading, orientation, ref_sys_bias, begin_clock_states, rec_clock, rec_sys_bias, rec_clock_rate, rec_sys_bias_rate, rec_clock_rate_gm, rec_sys_bias_rate_gm, sat_clock, sat_clock_rate, sat_clock_rate_gm, end_clock_states, trop, trop_grad, trop_model, ionospheric, iono_stec, rec_pco_x, rec_pco_y, rec_pco_z, sat_pco_x, sat_pco_y, sat_pco_z, rec_pcv, ant_delta, eop, eop_rate, calc, slr_rec_range_bias, slr_rec_time_bias, xform_xlate, xform_rtate, xform_scale, xform_delay, ambiguity, code_bias, phase_bias, z_amb, reference, begin_meas_states, code_meas, phas_meas, laser_meas, pseudo_meas, orbit_meas, filter_meas, end_meas_states, begin_orbit_states, orbit, cr, cd, emp_d_0, emp_d_1, emp_d_2, emp_d_3, emp_d_4, emp_y_0, emp_y_1, emp_y_2, emp_y_3, emp_y_4, emp_b_0, emp_b_1, emp_b_2, emp_b_3, emp_b_4, emp_r_0, emp_r_1, emp_r_2, emp_r_3, emp_r_4, emp_t_0, emp_t_1, emp_t_2, emp_t_3, emp_t_4, emp_n_0, emp_n_1, emp_n_2, emp_n_3, emp_n_4, emp_p_0, emp_p_1, emp_p_2, emp_p_3, emp_p_4, emp_q_0, emp_q_1, emp_q_2, emp_q_3, emp_q_4, end_orbit_states, begin_inertial_states, gyro_bias, gyro_scale, accl_bias, accl_scale, imu_offset, end_inertial_states, range]
used_predictions: # Filter states to retrieve from mongo [none, one, all, rec_pos, rec_vel, rec_pos_rate, rec_acc, strain_rate, pos, vel, acc, heading, orientation, ref_sys_bias, begin_clock_states, rec_clock, rec_sys_bias, rec_clock_rate, rec_sys_bias_rate, rec_clock_rate_gm, rec_sys_bias_rate_gm, sat_clock, sat_clock_rate, sat_clock_rate_gm, end_clock_states, trop, trop_grad, trop_model, ionospheric, iono_stec, rec_pco_x, rec_pco_y, rec_pco_z, sat_pco_x, sat_pco_y, sat_pco_z, rec_pcv, ant_delta, eop, eop_rate, calc, slr_rec_range_bias, slr_rec_time_bias, xform_xlate, xform_rtate, xform_scale, xform_delay, ambiguity, code_bias, phase_bias, z_amb, reference, begin_meas_states, code_meas, phas_meas, laser_meas, pseudo_meas, orbit_meas, filter_meas, end_meas_states, begin_orbit_states, orbit, cr, cd, emp_d_0, emp_d_1, emp_d_2, emp_d_3, emp_d_4, emp_y_0, emp_y_1, emp_y_2, emp_y_3, emp_y_4, emp_b_0, emp_b_1, emp_b_2, emp_b_3, emp_b_4, emp_r_0, emp_r_1, emp_r_2, emp_r_3, emp_r_4, emp_t_0, emp_t_1, emp_t_2, emp_t_3, emp_t_4, emp_n_0, emp_n_1, emp_n_2, emp_n_3, emp_n_4, emp_p_0, emp_p_1, emp_p_2, emp_p_3, emp_p_4, emp_q_0, emp_q_1, emp_q_2, emp_q_3, emp_q_4, end_orbit_states, begin_inertial_states, gyro_bias, gyro_scale, accl_bias, accl_scale, imu_offset, end_inertial_states, range]
debug: ⯆ # Debug options are designed for developers and should probably not be used by normal users
check_plumbing: # Debugging option to show sizes of objects in memory to detect leaks
explain_measurements: # Debugging option to show verbose measurement coefficients
fatal_message_level: # Threshold level for exiting the program early (0-2)
mincon_filename: # Filename of pre-mincon filter state for backup/loading
mincon_only: # Debugging option to re-run minimum constraints code
output_mincon: # Debugging option to only save pre-minimum constraints filter state
retain_rts_files: # Debugging option to keep rts files for post processing
rts_only: # Debugging option to only re-run rts from previous run
check_broadcast_differences:
compare_attitudes:
compare_clocks:
compare_orbits: